Assessing population connectivity is central to understanding population dynamics, and is therefore of great importance in evolutionary biology and conservation biology. In the marine realm, the apparent absence of physical barriers, large population sizes and high dispersal capacities of most organisms often result in no detectable structure, thereby hindering inferences of population connectivity. In a review paper, Gagnaire et al.  propose several ideas to improve detection of population connectivity. Notably, using simulations they show that under certain circumstances introgression from one species into another may reveal cryptic population structure within that second species.
The isolated Kerguelen archipelago in the south of Indian Ocean represents a typical situation where the structure of coastal marine organisms is expected to be difficult to detect. In an elegant genomic study, Fraïsse et al.  take advantage of introgression from foreign lineages to infer fine-grained population structure in a population of mussels around the Kerguelen archipelago, and investigate its association with environmental variables. Using a large panel of genome-wide markers (GBS) and applying a range of methods that unravel patterns of divergence and gene flow among lineages, they first find that the Kerguelen population is highly admixed, with a major genetic background corresponding to the southern mussel lineage Mytilus platensis introgressed by three northern lineages. By selecting a panel of loci enriched in ancestry-informative SNPs (ie, SNPs highly differentiated among northern lineages) they then detect a fine-scale genetic structure around the Kerguelen archipelago, and identify a major connectivity break. They further show an associating between the genetic structure and environmental variables, particularly the presence of Macrocystis kelp, a marker of habitat exposure to waves (a feature repeatedly evidenced to be important for mussels). While such association pattern could lead to the interpretation that differentiated SNPs correspond to loci directly under selection or linked with such loci, and even be considered as support for adaptive introgression, Fraïsse et al.  convincingly show by performing simulations that the genetic-environment association detected can be entirely explained by dispersal barriers associated with environmental variables (habitat-associated connectivity). They also explain why the association is better detected by ancestry-informative SNPs as predicted by Gagnaire et al. . In addition, intrinsic genetic incompatibilities, which reduce gene flow, tend to become trapped at ecotones due to ecological selection, even when loci causing genetic incompatibilities are unlinked with loci involved in adaption to local ecological conditions (Bierne et al. ’s coupling hypothesis), leading to correlations between environmental variables and loci not involved in local adaptation. Notably, in Fraïsse et al. ’s study, the association between the kelp and ancestry-informative alleles is not consistent throughout the archipelago, casting further doubt on the implication of these alleles in local adaptation.
The study of Fraïsse et al.  is therefore an important contribution to evolutionary biology because 1) it provides an empirical demonstration that alleles of foreign origin can be pivotal to detect fine-scale connectivity patterns and 2) it represents a test case of Bierne et al. ’s coupling hypothesis, whereby introgressed alleles also enhance patterns of genetic-environment associations. Since genomic scan or GWAS approaches fail to clearly reveal loci involved in local adaptation, how can we disentangle environment-driven selection from intrinsic reproductive barriers and habitat-associated connectivity? A related question is whether we can reliably identify cases of adaptive introgression, which have increasingly been put forward as a mechanism involved in adaptation . Unfortunately, there is no easy answer, and the safest way to go is to rely – where possible – on independent information , in particular functional studies of the detected loci, as is for example the case in the mimetic butterfly Heliconius literature (e. g., ) where several loci controlling colour pattern variation are well characterized.
 Gagnaire, P.-A., Broquet, T., Aurelle, D., Viard, F., Souissi, A., Bonhomme, F., Arnaud-Haond, S., & Bierne, N. (2015). Using neutral, selected, and hitchhiker loci to assess connectivity of marine populations in the genomic era. Evolutionary Applications, 8, 769–786. doi: 10.1111/eva.12288
 Fraïsse, C., Haguenauer, A., Gerard, K., Weber, A. A.-T., Bierne, N., & Chenuil, A. (2018). Fine-grained habitat-associated genetic connectivity in an admixed population of mussels in the small isolated Kerguelen Islands. bioRxiv, 239244, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/239244
 Bierne, N., Welch, J., Loire, E., Bonhomme, F., & David, P. (2011). The coupling hypothesis: why genome scans may fail to map local adaptation genes. Molecular Ecology, 20, 2044–2072. doi: 10.1111/j.1365-294X.2011.05080.x
 Hedrick, P. W. (2013). Adaptive introgression in animals: examples and comparison to new mutation and standing variation as sources of adaptive variation. Molecular Ecology, 22, 4606–4618. doi: 10.1111/mec.12415
 Ravinet, M., Faria, R., Butlin, R. K., Galindo, J., Bierne, N., Rafajlović, M., Noor, M. A. F., Mehlig, B., & Westram, A. M. (2017). Interpreting the genomic landscape of speciation: a road map for finding barriers to gene flow. Journal of Evolutionary Biology, 30, 1450–1477. doi: 10.1111/jeb.13047.
 Jay, P., Whibley, A., Frézal, L., Rodríguez de Cara, M. A., Nowell, R. W., Mallet, J., Dasmahapatra, K. K., & Joron, M. (2018). Supergene evolution triggered by the introgression of a chromosomal inversion. Current Biology, 28, 1839–1845.e3. doi: 10.1016/j.cub.2018.04.072
Dear Dr Fraisse,
I am pleased to announce that, after assessment of your revised version by the two referees, we will recommend your paper, pending minor revisions asked by Thomas Broquet. Both referees acknowledge major improvements in this version, so congratulations for the good job! Best regards,
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-Details on quantitative analyses (e.g., data treatment and statistical scripts in R, bioinformatic pipeline scripts, etc.) and details concerning simulations (scripts, codes) must be available to readers in the text, as appendices, or through an open data repository, such as Zenodo, Dryad or some other institutional repository. The scripts or codes must be carefully described so that they can be reused.
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Dear Dr Fraïsse,
The same referees (Tatiana Giraud and Thomas Broquet) have evaluated the revised version of your manuscript. While they acknowledge improvements and consider the manuscript original and of general interest, there are still many concerns. I outline below the main points that need to be addressed carefully. The reviews provide more details on those, as well as additional comments, which should be addressed thoroughly.
Focus of the manuscript: this has obviously improved since the first version, but only in some places. Notably, the introduction and the discussion of the manuscript are still very tuned on adaptation, as if the main goal of the paper were to investigate adaptive introgression (several examples are highlighted by the referees, and many more can be found throughout the text). As such, the current version looks a bit ‘schizophrenic’. Introduction (and, to a lesser extent, discussion) and the questions should be entirely reframed to refocus the manuscript around the fine-scale genetic structure in the Kerguelen and the effect of introgression from foreign lineages (see the suggestions of both referees in this respect). Adaptive introgression can of course be mentioned (briefly in introduction), and discussed (in light of your results and literature on the topic in the discussion), but as pointed out at the previous round of reviews and here again, your data do not enable to test for adaptive introgression. Also, the manuscript lacks a general conclusion that extends beyond mussels.
General clarity: as highlighted by both referees, the manuscript is complex, and lacks clarity in many instances. In addition to streamlining the text, improving general consistency and making sure appropriate vocabulary/phrasing is used, you should find a way to outline in a clear way 1) the hypotheses you want to test, 2) the data you have (you have different sets of samples and different sets of markers, which makes things hard to follow) and 3) the methods you implement for each hypothesis test. This could take the form of a kind of a synthetic section at the beginning of Material and Methods, and/or a figure, or something else, but it is essential that those methodological aspects are clarified.
Tatiana Giraud raised an important point during the first round of reviews, which has not been satisfactorily addressed: please clearly explain (in the ms, not only in the reply to the referees) on what principle the methods used for that purpose disentangle ILS from introgression, so that the reader can assess how reliable the inference is.
Another point raised by Tatiana Giraud relates to accounting for spatial distances in the environment-genotype analyses. This is done by using spatial coordinates, while mussel dispersal around the Kerguelen is likely more constrained by coastal distances. I don’t know if even a crude measure of such distance can be incorporated in the analyses, but if not, the discussion should not be so affirmative that spatial structure does not have any effect.
Although Thomas Broquet acknowledges the improvement provided by the new simulations to the link between fine-scale genetic structure and connectivity break (in the absence of local adaptation), he calls for caution for interpreting the results because of the lack of confidence intervals. Additionally, although the different analyses that enable to assess gene flow do indicate introgression from northern species (provided that these analyses are reliable), the patterns detected are somewhat different (e. g., introgression from galloprovincialis (Treemix) versus mostly edulis (Twisst) or both (dadi); see also Thomas Broquet’s comment on the ancient gene flow inferred by dadi versus claims of secondary contact). Why do you think this is so?
A few minor comments, in addition to those mentioned in the review:
L. 53: should read ‘thereby generally showing’, or ‘therefore they generally show’
L. 217: please briefly explain how KASPar works.
L. 263 and below: the ‘delta’ of dadi is not written the same way as in the results.
L. 342: you never mention how many demes you model.
L. 549: the % given are those of platensis, not edulis.
L. 559: should read ‘provided’.
L. 585: ‘in agreement’ with…?
L. 787: ‘our results are very promising that…’ doesn’t have a correct syntax.
Please check the text thoroughly for additional inconsistencies, typos etc that we may have missed.
Good luck with the revision,
Dear Dr Fraisse,
Your manuscript has now been assessed by two reviewers, Tatiana Giraud and Thomas Broquet. While both of them found the paper interesting, they also have important comments that need to be addressed before the paper can be reassessed for recommendation. Notably, Tatiana Giraud questions the ability of the methods used to discriminate between introgression and ILS, and Thomas Broquet is not convinced that the pattern of differentiation detected indicates local adaptation, rather than a physical barrier to dispersal. In line with this, Thomas Broquet also suggests changes in the focuses and structures of introduction and, to a lesser extent, discussion.
Both reviewers also have other minor points, particularly about clarifying the methods, which need to be addressed.
On the BioRxiv site, as noted by Thomas Broquet, the supplementary files do not correspond to the supplementary files mentioned in the MS (file S1, S2 and S3): a pdf file called 'Supplementary Information bioRxiv' and a xlsx file called 'Supplementary Information table’. This xlsx file cannot be opened.
Please make available all necessary information with a clear correspondance between the MS and the supplementary files, and please provide a correct table.
In addition, make sure that
-all the data are available to readers.
-all details of the quantitative analyses (e.g. data treatment and statistical scripts in R, bioinformatic pipelines scripts, etc.) are available to the readers, as appendices or supplementary online materials
Good luck with the manuscript revision,